ObjectiveTo analyze the diversity and structural characteristics of microbial communities in the rhizosphere soil of healthy and diseased Andrographis paniculata and to explore the interactions of soil， plants， and microorganisms during the occurrence of diseases. MethodsThe physicochemical properties of the rhizosphere soil of healthy and diseased A.paniculata were determined， and the composition and diversity of bacterial and fungal communities in the rhizosphere soil were analyzed by Illumina high-throughput sequencing. Furthermore， the correlations between physicochemical properties and microorganisms of the rhizosphere soil were explored. ResultsThe content of total nitrogen， total potassium， and available potassium in the rhizosphere soil of diseased A. paniculata was significantly higher than that of healthy A. paniculata. The alpha diversity and richness （operational taxonomic units） of bacterial and fungal communities in the rhizosphere soil of diseased plants decreased compared with those of healthy plants. The microbial communities in the rhizosphere soil of healthy and diseased A. paniculata showed similar composition but different relative abundance. At the phylum level， the relative abundance of Proteobacteria and Chytridiomycota significantly increased， while that of Bacteroidota significantly decreased in the rhizosphere soil of diseased plants. At the genus level， the relative abundance of Sphingomonas， Pseudomonas， and Bryobacter significantly increased， while that of RB41 showed a significant decrease in the rhizosphere soil of diseased plants. The correlation analysis showed different correlations of microbial phyla with physicochemical properties of the rhizosphere soil between healthy and diseased plants. Organic matter， alkaline nitrogen， available phosphorus， and total potassium were correlated with the relative abundance of some dominant bacterial and fungal phyla in the rhizosphere soil of healthy plants， while available nitrogen and total phosphorus were correlated with the relative abundance of some dominant bacterial and fungal phyla in the rhizosphere soil of diseased plants. ConclusionThere are differences in the diversity and richness of microbial communities in the rhizosphere soil of healthy and diseased A. paniculata. The physicochemical properties of soil may have an impact on the rhizosphere microorganisms of A. paniculata， leading to the development of diseases. The results provide a scientific basis for the prevention and ecological management of A. paniculata diseases.

ObjectiveTo analyze the diversity and structural characteristics of microbial communities in the rhizosphere soil of healthy and diseased Andrographis paniculata and to explore the interactions of soil， plants， and microorganisms during the occurrence of diseases. MethodsThe physicochemical properties of the rhizosphere soil of healthy and diseased A.paniculata were determined， and the composition and diversity of bacterial and fungal communities in the rhizosphere soil were analyzed by Illumina high-throughput sequencing. Furthermore， the correlations between physicochemical properties and microorganisms of the rhizosphere soil were explored. ResultsThe content of total nitrogen， total potassium， and available potassium in the rhizosphere soil of diseased A. paniculata was significantly higher than that of healthy A. paniculata. The alpha diversity and richness （operational taxonomic units） of bacterial and fungal communities in the rhizosphere soil of diseased plants decreased compared with those of healthy plants. The microbial communities in the rhizosphere soil of healthy and diseased A. paniculata showed similar composition but different relative abundance. At the phylum level， the relative abundance of Proteobacteria and Chytridiomycota significantly increased， while that of Bacteroidota significantly decreased in the rhizosphere soil of diseased plants. At the genus level， the relative abundance of Sphingomonas， Pseudomonas， and Bryobacter significantly increased， while that of RB41 showed a significant decrease in the rhizosphere soil of diseased plants. The correlation analysis showed different correlations of microbial phyla with physicochemical properties of the rhizosphere soil between healthy and diseased plants. Organic matter， alkaline nitrogen， available phosphorus， and total potassium were correlated with the relative abundance of some dominant bacterial and fungal phyla in the rhizosphere soil of healthy plants， while available nitrogen and total phosphorus were correlated with the relative abundance of some dominant bacterial and fungal phyla in the rhizosphere soil of diseased plants. ConclusionThere are differences in the diversity and richness of microbial communities in the rhizosphere soil of healthy and diseased A. paniculata. The physicochemical properties of soil may have an impact on the rhizosphere microorganisms of A. paniculata， leading to the development of diseases. The results provide a scientific basis for the prevention and ecological management of A. paniculata diseases.

Objective : To explore whether chrysophanol(CHR) affects macrophage polarization by promoting mitochondrial biosynthesis through AMPK/PGC-1α pathway. Methods : The molecular docking and binding ability of CHR with AMPK and PGC-1α were predicted by Autodock vina software. Human monocytes(THP-1) were induced to M0 macrophages by phorbol myristate acetate(PMA), and to M1 macrophages by lipopolysaccharide(LPS) combined with interferon-γ(IFN-γ), which were set as Control group. M1 macrophages treated with CHR were set as CHR group. M1 macrophages treated with CHR combined with AMPK inhibitor(Compound C) were set as CHR+Compound C group. The mRNA expression levels of M1 macrophage markers(iNOS, CD86) and mitochondrial biosynthesis related genes(PGC-1α, NFR-1, TFAM) were detected by Quantitative real time polymerase chain reaction(qRT-PCR). The expression level of M1 macrophage marker iNOS was detected by immunofluorescence. The protein expression levels of AMPK, p-AMPK and PGC-1α were detected by Western blot. Results : The docking results showed that the binding energies of CHR with AMPK and PGC-1α were-8.4 kcal/mol and-7.4 kcal/mol, respectively. qRT-PCR results showed that the in vitro model of M1 macrophages was successfully established. Compared with the Control group, CHR treatment significantly increased the mRNA expression of mitochondrial biosynthesis-related genes PGC-1α, NFR-1, and TFAM(P<0.001). Compared with CHR treatment group, CHR combined with Compound C treatment significantly decreased the mRNA expression levels of mitochondrial biosynthesis-related genes PGC-1α, NFR-1, and TFAM(P<0.05). Immunofluorescence results showed that CHR treatment inhibited the protein expression of iNOS compared with the Control group(P<0.001). Compared with CHR treatment group,CHR combined with Compound C treatment reversed the inhibitory effect of CHR on i NOS protein expression(P<0.05). Western blot results showed that compared with the Control group,the CHR treatment group had significant increase in the protein expression levels of p-AMPK and PGC-1α(P<0.001).Compared with CHR treatment group,CHR combined with Compound C treatment significantly decreased the protein expression levels of p-AMPK and PGC-1α(P<0.05). Conclusion Chrysophanol may inhibit macrophage polarization to M1 by activating AMPK/PGC-1α signaling pathway to promote mitochondrial biosynthesis.

Objective To study the plasma metabolomics of mice poisoned by different dosage of the combination of diazepam and ethanol,and to reveal the toxicological mechanisms of combined poisoning of diazepam and ethanol.Methods Female Kunming mice were randomly divided into blank group,single and combined poisoning group(n=6),Based on the LD50 of diazepam co-administered with graded ethanol doses,mice in the single-drug and combined groups received oral gavage at 1/2,1,and 2 × LD50.Retro-orbital blood samples(～500 μL)were collected within 24 hours post-administration and analyzed by UPLC-QE-MS technology.Principal component analysis and orthogonal partial least squares discriminant analysis were used to identify differential metabolites and associated metabolic pathways.Results A total of 387 differential metabolites were identified in the combined poisoning group of diazepam and ethanol implicating the key pathways including tryptophan metabolism,phenylalanine metabolism,arginine and proline metabolism,Glycerophospholipid metabolism,phenylalanine,tyrosine and tryptophan biosynthesis.Conclusion Combined diazepam and ethanol poisoning exerts significant systemic effects by disrupting neurotransmitters conduction,exacerbating oxidative stress response and dysregulating energy metabolism.

Diseases caused by animal virus infection seriously restricts the healthy development of animal husbandry.In-depth study of the molecular mechanism of viral replication and pathogenesis will provide theoretical basis for screening vaccine and drug targets.N6-methyladenosine(m6 A)modification occurs extensively in viral and host transcriptomes and affects viral replication and pathogenicity by regulating gene expression,which acts as a novel regulator of gene expression in addition to DNA and protein modifications.Insight into the regulatory molecular mechanism of m6 A modification in virus infection is the research hotspots and frontiers.In recent years,there are re-ports of alternation of the m6 A modification-associated epitranscriptomes and related function a-nalysis during virus infection.Here,we summarize the alternation of the epitranscriptomes induced by African swine fever virus(ASFV),porcine reproductive and respiratory syndrome virus(PRRSV),porcine epidemic diarrhoea virus(PEDV),cestode virus(CSFV),porcine pseudorabies virus(PRV),Marek's disease virus(MDV),Newcastle disease virus(NDV),avian leukaemia virus(ALV)and duck hepatitis A virus(DHAV)infection,and the subsequent effects on viral replica-tion and pathogenicity.We also discuss the potential role and molecular mechanism of m6 A modification in animal virus replication and pathogenesis,which will contributes to the prevention and control for animal disease.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objectives:To investigate the physical growth status of pediatric patients with transfusion-dependent thalassemia (TDT) and analyze the effects of treatment-related and socioeconomic factors on physical growth.Methods:Based on the specialized thalassemia database from gene therapy clinical research at the Institute of Hematology & Hospital of Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, we collected data on height and weight development, family economic status, and medical records of 338 pediatric patients with TDT from October 2023 to May 2024. The length/height-for-age and body mass index (BMI) -for-age were classified based on the Growth Standard for Children under 7 Years of Age, Standard for Height Level Classification among Children and Adolescents Aged 7-18 Years, and Dietary Guidelines for Chinese Residents. Logistic regression analysis was conducted to assess the effects of family economic status and disease-related treatment on length/height-for-age and BMI-for-age.Results:Among the 338 patients, 118 were children and 220 were adolescents (192 males and 146 females), with a median age of 12 years (range: 0.8-18) and a median diagnosis duration of 10.3 years (range: 0.5-17.9). Subtypes included α-thalassemia [21 cases (6.2%) ], β-thalassemia [288 cases (85.2%) ], and combined αβ-thalassemia[29 cases (8.6%) ]. The monthly household income of patients was concentrated in 3 000-5 000 yuan (39.9%) and 5 001-10 000 yuan (34.9%), whereas 67.2% of the families had monthly medical expenses of <3 000 yuan. Of the patients, 75.5% received their first transfusion before 1 year of age. The proportions of children and adolescents with pretransfusion hemoglobin (HGB) of ≤70 g/L were 4.2% and 6.4%, respectively. Adolescents demonstrated significantly higher rates of transfusion frequency of <4 weeks/session, monthly red blood cell infusion of >2 U, serum ferritin (SF) of ≥5 000 μg/L, iron chelation therapy, and splenectomy compared with children (all P<0.05). Of the 338 patients, 26.0%, 22.8%, and 8.9% demonstrated stunted growth, underweight, and concurrent stunted growth with underweight, respectively. No significant difference was observed in the stunted growth rates between children (22.9%) and adolescents (27.7%) ( P=0.402). However, the underweight rate in adolescents (26.8%) was significantly higher than that in children (15.3%) ( P=0.023). The multivariate analysis determined the following risk factors for stunted growth: monthly household income of <10 000 yuan (5 001-10 000 yuan: OR=5.49, 95% CI: 1.48-35.76; 3 000-5 000 yuan: OR=6.87, 95% CI: 1.88-44.60; <3 000 yuan: OR=9.29, 95% CI: 2.20-64.77), pretransfusion HGB of ≤70 g/L ( OR=3.25, 95% CI: 1.07-10.18), and SF of ≥5 000 μg/L ( OR = 3.04, 95% CI: 1.20-7.70). Longer diagnostic duration was associated with underweight ( OR=1.10, 95% CI: 1.01-1.20) . Conclusions:Children and adolescents with TDT with pretransfusion SF of ≥5 000 μg/L, HGB of ≤70 g/L, low monthly household income, or longer diagnosis duration were significantly more likely to experience delayed physical growth.

Objective:To investigate alterations in the immune lineage of T-cell large granular lymphocytic leukemia (T-LGLL) at the single-cell transcriptome level and to elucidate its pathogenic mechanisms.Methods:Peripheral blood samples were collected from 5 T-LGLL patients before and after treatment (from June 2019 to December 2020) and 3 healthy controls at the Institute of Hematology & Blood Diseases Hospital, CAMS & PUMC. Single-cell transcriptome sequencing libraries were prepared and sequenced using 10× Genomics technology. Differentially expressed genes in immune cells were compared between patients and healthy donors, followed by pathway enrichment analyses.Results:Profiling 67,237 immune cells revealed that, in T-LGLL: 1) Effector CD8+ T cells exhibited increased numbers, enhanced cytotoxicity, and greater proliferative capacity. Following effective immunosuppressive therapy, both the proliferative capacity and effector functions of these cells significantly decreased ( P<0.05). 2) The proportion of regulatory T (Treg) cells was reduced, accompanied by increased apoptosis. After effective immunosuppressive therapy leading to remission, Treg cell proportions increased, and apoptotic pathways were downregulated ( P<0.05). 3) Antigen-presenting cells (APCs) showed enhanced functionality. Monocytes and dendritic cells were enriched in antigen synthesis and presentation pathways, while B cells displayed increased antigen-binding capacity and were enriched in pathways related to T-cell activation ( P<0.05). 4) Natural killer (NK) cells exhibited attenuated cytotoxic function but demonstrated an enhanced regulatory capacity over T cells ( P<0.05) . Conclusions:T-LGLL patients present a characteristic immunological profile marked by an imbalance in immune homeostasis. This profile includes abnormal activation and expansion of effector CD8 + T cells, and a reduction in Treg cell numbers accompanied by functional impairment. Furthermore, APCs and NK cells were found to positively regulate T-lymphocyte activation, differentiation, and proliferation.

Objective:To explore the application value of radiomics, deep learning, and their combined models in predicting the efficacy of radioiodine adjuvant therapy in patients with papillary thyroid cancer (PTC).Methods:A retrospective analysis was conducted on the clinical and imaging data of 131 PTC patients (38 males, 93 females; age 41(33, 48) years) who received first 131I treatment at the Affiliated Hospital of Guilin Medical University from January 2018 to March 2023. Patients were randomly divided into a training set ( n=105) and a test set ( n=26) at the ratio of 8∶2. Multivariate logistic regression analysis was used to screen clinical features to determine independent predictors affecting the efficacy of 131I therapy. Radiomics and deep learning features were extracted from the enhanced CT scans and were combined by using the extremely randomized trees (ExtraTrees) algorithm to construct radiomics, deep learning, and combined models. The predictive abilities of the models were evaluated by AUC, and the Delong test was applied to compare the difference between AUCs. Results:Higher pre-ablation stimulated thyroglobulin (ps-Tg) levels (odds ratio( OR)=1.060, 95% CI: 1.025-1.095, P=0.004) and bilateral lesions ( OR=5.085, 95% CI: 1.452-17.814, P=0.033) were independent predictors of the efficacy of 131I therapy in intermediate to high-risk PTC patients. In the training set, the radiomics model (AUC=0.853) and combined model (AUC=0.880) significantly outperformed the deep learning model (AUC=0.711; Z values: 2.48, 3.09, P values: 0.013, 0.002), while there was no statistically significant difference between the radiomics and combined models ( Z=0.51, P=0.610). In the test set, AUCs of the radiomics, deep learning, and combined models were 0.746, 0.624, and 0.876, respectively, and the AUC of the combined model was higher than that of the radiomics model or deep learning model ( Z values: 2.05, 1.99, P values: 0.040, 0.047). Conclusion:The combined model demonstrates superior performance over the standalone radiomics model and deep learning model in predicting the efficacy of 131I treatment in PTC patients.

Objective:To evaluate the efficacy of cyclophosphamide in patients with T-cell large granular lymphocytic leukemia (T-LGLL) and the maintenance of treatment-free remission (TFR) following drug discontinuation.Methods:Clinical data were collected from 37 patients with T-LGLL who received oral cyclophosphamide at the Regenerative Medicine Clinic of the Institute of Hematology and Blood Diseases Hospital between June 2019 and March 2024. Patient clinical characteristics, treatment efficacy, and long-term TFR were analyzed.Results:The median age of the 37 patients was 60 years (range: 37-86), and 22 (59.5%) were male. Anemia was observed in 30 patients (81.1%), and 28 (75.7%) met the diagnostic criteria for secondary pure red cell aplasia. Neutropenia occurred in 15 patients (40.5%), lymphocytosis in 11 (29.7%), and thrombocytopenia in three (8.1%). Sixteen patients (43.2%) had not received prior immunosuppressive therapy (treatment-naive group), while 21 patients (56.8%) were refractory to or had relapsed after immunosuppressive treatment (refractory/relapsed group). All patients met the treatment criteria and received oral cyclophosphamide at doses of 50-100 mg/day. Among the 36 evaluable patients, hematologic remission was achieved in 25 (69.4%), with a median time of 2.0 months (range: 0.7-7.0). There was no statistically significant difference in remission rates between the treatment-naive and refractory/relapsed groups (68.5% vs. 66.7%, P=0.589). Among the 25 patients who achieved hematologic remission, 24 discontinued cyclophosphamide. With a median follow-up of 39.0 months (range: 8.0-56.0), the median TFR duration was not reached. The estimated TFR rates were (90.87± 6.16) % at 12 months and (75.72±11.04) % at 36 months. No significant difference in TFR was observed between the treatment-naive and refractory/relapsed groups ( P=0.451) . Conclusion:Oral cyclophosphamide is effective in the treatment of T-LGLL, and patients may maintain long-term TFR following drug discontinuation.